The Hasso Plattner Institute offers a practically-oriented computer science study program at an internationally recognized institute. This study includes the Germany-wide unique "IT-Systems Engineering" program and the new master programs: "Digital Health", "Data Engineering", and "Cybersecurity."

Research at the Hasso Plattner Institute is characterized by standards of scientific excellence, practical relevance and close cooperation with industry and society. Outstanding research results are achieved in the fields of specialization, in excellent research programs and at the international Research School.

The Hasso Plattner Institute in Potsdam is unique on the German academic landscape. The institute's program continues to grow with the support of its founder Hasso Plattner and through international cooperation. Find out more about the founder, events and studies at HPI.

The Hasso Plattner Institute has educational programs for both high school students and working professionals. It operates its own IT learning platform - openHPI - which provides free online courses. The Youth Academy organizes computer science camps and events for high school students. Professionals can take advantage of educational opportunities in the field of Design Thinking at the HPI Academy.

Description

This course is designed to give students an in-depth introduction to machine learning. The lectures and exercises are designed around biomedical use cases and will use real-world biomedical data to gain practical experience with machine learning models and algorithms. The course will start with an introduction to the basic concepts of machine learning and empirical data analysis, such as model fitting, selection and validation. During the second part of the course, we will discuss supervised machine learning, starting with linear models, to non-linear models, including deep neural networks and convolutional neural networks for medical imaging. During the third part of the course, we will discuss unsupervised learning, starting with clustering, to dimensionality reduction and latent variable models. While we will discuss machine learning in a biomedical context, the learned principles and algorithms are applicable to other fields as well.

Learning Objectives:

Understand concepts, methods and algorithms in machine learning

Ability to empirically analyze real-world data

Ability to assess the quality and validity of a machine learning model for a given analysis

Ability to select, develop, implement and apply appropriate machine learning models and algorithms for a given use case.

Gain an introduction to biomedical use cases of machine learning, including clinical prediction problems, medical image analysis, and modeling of multi-omics data.

Course Syllabus and Schedule (Summer 2019)

Please note that the schedule is still preliminary and details are subject to change.